Design of a Computational Model for 3D Concrete Printed Geometry Using Machine Learning and Genetic Optimization

Authors

  • Jurij Ličen

    Faculty of Architecture, University of Ljubljana, 1000 Ljubljana, Slovenia
  • Taole Chen

    Independent Researcher, 1120 Vienna, Austria

DOI:

https://doi.org/10.30564/jbms.v8i2.13096
Received: 24 February 2026 | Revised: 12 March 2026 | Accepted: 11 May 2026 | Published Online: 12 June 2026

Abstract

3D Concrete Printing (3DCP) is an emerging technology with well-established benefits and the potential to dramatically change the construction industry. While research in material and process optimization is gaining traction, the architectural application of 3DCP remains relatively underdeveloped. Among many challenges, a lack of suitable computational modelling techniques is often identified as a major obstacle, resulting in simplistic design solutions that do not take full advantage of 3DCP technology. This study proposes a fabrication-aware design model using machine learning (ML), specifically genetic optimization, to address the research gap. 3DCP is used to produce sacrificial formwork for freeform reinforced concrete shell structures. The model conceptualizes a module-based approach to establish interlinked feedback loops across the various stages of a project, enabling fabrication and assembly considerations in the early design phase. Structural behaviour, printability, and segmentation constraints are translated into evaluative criteria within a unified computational workflow implemented in Rhino/Grasshopper, using the Galapagos genetic optimization solver. The framework enables iterative exploration of design options while accounting for both geometric and fabrication-related constraints. Three shell typologies are used to demonstrate the method, including a cantilever, a bridge, and a wall element, supported by a full-scale 3D printed segment for initial validation. This approach enables designers to develop geometries that are specifically tailored to the constraints and opportunities of 3DCP, opening new possibilities for meaningful interaction with the design-to-fabrication pipeline of complex 3DCP geometries.

Keywords:

3D Concrete Printing; Computational Design; Evolutionary Optimization; Fabrication Constraints; Reinforced Concrete Shells; Digital Fabrication; Early-Stage Design

References

[1] Manzoor, B., Othman, I., Pomares, J.C., 2021. Digital Technologies in the Architecture, Engineering and Construction (AEC) Industry: A Bibliometric-Qualitative Literature Review of Research Activities. International Journal of Environmental Research and Public Health. 18(11), 6135. DOI: https://doi.org/10.3390/ijerph18116135

[2] Thomsen, M.R., Nicholas, P., Tamke, M., et al., 2020. Towards Machine Learning for Architectural Fabrication in the Age of Industry 4.0. International Journal of Architectural Computing. 18(4), 335–352. DOI: https://doi.org/10.1177/1478077120948000

[3] El Hage, A., Paquet, E., Neu, T., et al., 2024. Navigating the Digital Chain in Concrete 3D Printing. arXiv preprint. arXiv:2410.16319. DOI: https://doi.org/10.48550/ARXIV.2410.16319

[4] Skoury, L., Leder, S., Menges, A., et al., 2024. Digital Twin Architecture for the AEC Industry: A Case Study in Collective Robotic Construction. In Proceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems, Linz, Austria, 22–27 September 2024; pp. 413–418. DOI: https://doi.org/10.1145/3652620.3688255

[5] Canestrino, G., 2021. Considerations on Optimization as an Architectural Design Tool. Nexus Network Journal. 23, 919–931. DOI: https://doi.org/10.1007/s00004-021-00563-y

[6] Batikha, M., Jotangia, R., Baaj, M.Y., et al., 2022. 3D Concrete Printing for Sustainable and Economical Construction: A Comparative Study. Automation in Construction. 134, 104087. DOI: https://doi.org/10.1016/j.autcon.2021.104087

[7] Buswell, R.A., da Silva, W.R.L., Bos, F.P., et al., 2020. A Process Classification Framework for Defining and Describing Digital Fabrication with Concrete. Cement and Concrete Research. 134, 106068. DOI: https://doi.org/10.1016/j.cemconres.2020.106068

[8] Bhooshan, S., 2017. Parametric Design Thinking: A Case-Study of Practice-Embedded Architectural Research. Design Studies. 52, 115–143. DOI: https://doi.org/10.1016/j.destud.2017.05.003

[9] Yang, W., Wang, L., Ma, G., et al., 2023. An Integrated Method of Topological Optimization and Path Design for 3D Concrete Printing. Engineering Structures. 291, 116435. DOI: https://doi.org/10.1016/j.engstruct.2023.116435

[10] Tuvayanond, W., Prasittisopin, L., 2023. Design for Manufacture and Assembly of Digital Fabrication and Additive Manufacturing in Construction: A Review. Buildings. 13(2), 429. DOI: https://doi.org/10.3390/buildings13020429

[11] Lynn, G. (Ed.), 2016. Log 36: Robolog. Anyone Corporation: New York, NY, USA.

[12] Lim, S., Buswell, R.A., Le, T.T., et al., 2012. Developments in Construction-Scale Additive Manufacturing Processes. Automation in Construction. 21, 262–268. DOI: https://doi.org/10.1016/j.autcon.2011.06.010

[13] Bhooshan, S., Bhooshan, V., Dell'Endice, A., et al., 2022. The Striatus Bridge: Computational Design and Robotic Fabrication of an Unreinforced, 3D-Concrete-Printed, Masonry Arch Bridge. Architectural Structures and Construction. 2, 521–543. DOI: https://doi.org/10.1007/s44150-022-00051-y

[14] Wu, H., Li, Z., Zhou, X., et al., 2022. Digital Design and Fabrication of a 3D Concrete Printed Funicular Spatial Structure. In Proceedings of the 27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Sydney, Australia, 9–15 April 2022; pp. 71–80. DOI: https://doi.org/10.52842/conf.caadria.2022.2.071

[15] Ozdemir, D., 2021. Germany’s First 3D-Printed Residential Building Is Near Completion. Available from: https://interestingengineering.com/germanys-first-3d-printed-residential-building-is-near-completion (cited 18 July 2021).

[16] Allan, D., 2019. 3D Printers Can Build a House in 2 Days—and Could Solve Homelessness. Available from: https://www.techradar.com/in/news/3d-printers-can-build-a-house-in-2-days-and-could-solve-homelessness (cited 18 July 2021).

[17] Dreith, B., 2022. ICON and Lake Flato Build 3D-Printed House Zero in Austin. Available from: https://www.dezeen.com/2022/03/04/icon-lake-flato-3d-printed-house-zero-austin/ (cited 4 July 2023).

[18] Darko, A., Chan, A.P.C., Adabre, M.A., et al., 2020. Artificial Intelligence in the AEC Industry: Scientometric Analysis and Visualization of Research Activities. Automation in Construction. 112, 103081. DOI: https://doi.org/10.1016/j.autcon.2020.103081

[19] Özerol, G., Arslan Selçuk, S., 2023. Machine Learning in the Discipline of Architecture: A Review on the Research Trends between 2014 and 2020. International Journal of Architectural Computing. 21(1), 23–41. DOI: https://doi.org/10.1177/14780771221100102

[20] Campo, M.d., Leach, N. (Eds.), 2022. Machine Hallucinations: Architecture and Artificial Intelligence. John Wiley & Sons: London, UK.

[21] Peng, L., Miao, X., Zhu, J.-X., et al., 2025. Hybrid Machine Learning and Multi-Objective Optimization for Intelligent Design of Green and Low-Carbon Concrete. Sustainable Materials and Technologies. 45, e01605. DOI: https://doi.org/10.1016/j.susmat.2025.e01605

[22] Miao, X., Zhu, J.-X., Zhu, W.-B., et al., 2024. Intelligent Prediction of Comprehensive Mechanical Properties of Recycled Aggregate Concrete with Supplementary Cementitious Materials Using Hybrid Machine Learning Algorithms. Case Studies in Construction Materials. 21, e03708. DOI: https://doi.org/10.1016/j.cscm.2024.e03708

[23] Miao, X., Zhao, H., Peng, L., et al., 2025. Eco-Friendly Intelligent Mixture Design of Glass Powder Concrete: A Life Cycle Perspective with Hybrid Machine Learning and Generative Adversarial Networks. Journal of Building Engineering. 111, 113126. DOI: https://doi.org/10.1016/j.jobe.2025.113126

[24] Miao, X., Wang, Y., Hu, Z., et al., 2025. Valorization of Waste Glass into Sustainable Cementitious Materials: An Intelligent Approach for Fresh, Mechanical, and Durability Performance Assessment. Case Studies in Construction Materials. 22, e04822. DOI: https://doi.org/10.1016/j.cscm.2025.e04822

[25] Luo, D., Qiao, X., Niu, D., 2025. A Predictive Model for the Freeze-Thaw Concrete Durability Index Utilizing the Deeplabv3+ Model with Machine Learning. Construction and Building Materials. 459, 139788. DOI: https://doi.org/10.1016/j.conbuildmat.2024.139788

[26] Kabir, H., Wu, J., Dahal, S., et al., 2024. Automated Estimation of Cementitious Sorptivity via Computer Vision. Nature Communications. 15, 9935. DOI: https://doi.org/10.1038/s41467-024-53993-w

[27] Živković, M., Žujović, M., Milošević, J., 2023. 3D-Printed Architectural Structures Created Using Artificial Intelligences: A Review of Techniques and Applications. Preprints. 2023071826. DOI: https://doi.org/10.20944/preprints202307.1826.v1

[28] As, I., Pal, S., Basu, P., 2018. Artificial Intelligence in Architecture: Generating Conceptual Design via Deep Learning. International Journal of Architectural Computing. 16(4), 306–327. DOI: https://doi.org/10.1177/1478077118800982

[29] Nicholas, P., Rossi, G., Williams, E., et al., 2020. Integrating Real-Time Multi-Resolution Scanning and Machine Learning for Conformal Robotic 3D Printing in Architecture. International Journal of Architectural Computing. 18(4), 371–384. DOI: https://doi.org/10.1177/1478077120948203

[30] Tamke, M., Zwierzycki, M., Deleuran, A., et al., 2017. Lace Wall: Extending Design Intuition through Machine Learning. In: Menges, A., Sheil, B., Glynn, R., et al. (Eds.). Fabricate 2017. UCL Press: London, UK. pp. 98–105. DOI: https://doi.org/10.2307/j.ctt1n7qkg7.17

[31] Schoenauer, M., Hamda, H., Morel, P., 2005. Computational Chair Design Using Genetic Algorithms. Concept Magazine. 71(3), 95–99.

[32] Menges, A., 2012. Biomimetic Design Processes in Architecture: Morphogenetic and Evolutionary Computational Design. Bioinspiration & Biomimetics. 7(1), 015003. DOI: https://doi.org/10.1088/1748-3182/7/1/015003

[33] Frazer, J., 1995. An Evolutionary Architecture. Architectural Association: London, UK.

[34] Chaillou, S., 2019. AI + Architecture: Towards a New Approach [PhD Thesis]. Harvard University: Cambridge, MA, USA.

[35] Licen, J., Chen, T., 2024. Fabrication-Aware Design for 3DCP Shells Using Genetic Optimization. In Proceedings of the IASS 2024 Symposium, Zurich, Switzerland, 26–30 August 2024. Available from: https://app.iass2024.org/files/IASS_2024_Paper_353.pdf

[36] Vantyghem, G., De Corte, W., Shakour, E., et al., 2020. 3D Printing of a Post-Tensioned Concrete Girder Designed by Topology Optimization. Automation in Construction. 112, 103084. DOI: https://doi.org/10.1016/j.autcon.2020.103084

[37] Gosselin, C., Duballet, R., Roux, P., et al., 2016. Large-Scale 3D Printing of Ultra-High Performance Concrete—A New Processing Route for Architects and Builders. Materials & Design. 100, 102–109. DOI: https://doi.org/10.1016/j.matdes.2016.03.097

[38] Lin, Y., Bayramvand, A., Meibodi, M.A., 2023. Branch Wall: Developing Topology Informed Non-Planar Toolpath and Variable Deposition Rate for 3D Concrete Printing of Topology Optimized Load Bearing Wall. SSRN. DOI: https://doi.org/10.2139/ssrn.4663173

[39] Anton, A.-M., Reiter, L., Skevaki, E., 2022. Strategies for Integrating Straight Rebar in 3DCP Columns and Shear Walls. Open Conference Proceedings. 1, 21. DOI: https://doi.org/10.52825/ocp.v1i.81

[40] Bhooshan, S., 2023. Shape Design of 3D-Concrete-Printed Masonry Structures [PhD Thesis]. ETH Zurich: Zurich, Switzerland.

[41] Popescu, M., Rippmann, M., Liew, A., et al., 2021. Structural Design, Digital Fabrication and Construction of the Cable-Net and Knitted Formwork of the KnitCandela Concrete Shell. Structures. 31, 1287–1299. DOI: https://doi.org/10.1016/j.istruc.2020.02.013

Downloads

How to Cite

Ličen, J., & Chen, T. (2026). Design of a Computational Model for 3D Concrete Printed Geometry Using Machine Learning and Genetic Optimization. Journal of Building Material Science, 8(2), 77–92. https://doi.org/10.30564/jbms.v8i2.13096

Issue

Vol. 8, Iss. 2 (June 2026): in progress

Article Type

Article

License

Copyright © 2026 Jurij, Taole

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

Crossref
Scopus
Google Scholar
Europe PMC